Overview

Dataset statistics

Number of variables4
Number of observations316
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.3 KiB
Average record size in memory33.4 B

Variable types

Text1
Numeric1
Categorical2

Alerts

GAP_RISK_GRD_CD is highly overall correlated with GAP_XTN and 1 other fieldsHigh correlation
GAP_RISK_CN is highly overall correlated with GAP_XTN and 1 other fieldsHigh correlation
GAP_XTN is highly overall correlated with GAP_RISK_GRD_CD and 1 other fieldsHigh correlation
GEOM has unique valuesUnique

Reproduction

Analysis started2024-01-14 06:58:21.118458
Analysis finished2024-01-14 06:58:21.449482
Duration0.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

GEOM
Text

UNIQUE 

Distinct316
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-01-14T15:58:21.634235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length222
Median length221
Mean length188.72785
Min length181

Characters and Unicode

Total characters59638
Distinct characters25
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique316 ?
Unique (%)100.0%

Sample

1st rowMULTIPOLYGON (((129.424874234203 35.4811639065166,129.424878484996 35.481163838691,129.424878270076 35.4811548270706,129.424870386739 35.4811549528568,129.424874234203 35.4811639065166)))
2nd rowMULTIPOLYGON (((129.424869335713 35.4811525069518,129.424870386739 35.4811549528568,129.424878270076 35.4811548270706,129.424878055156 35.4811458154502,129.424874723312 35.481145868613,129.424869335713 35.4811525069518)))
3rd rowMULTIPOLYGON (((129.424885776598 35.481190767495,129.424890147148 35.4811906977582,129.424889932226 35.4811816861379,129.424881929132 35.4811818138357,129.424885776598 35.481190767495)))
4th rowMULTIPOLYGON (((129.424881929132 35.4811818138357,129.424889932226 35.4811816861379,129.424889717304 35.4811726745176,129.424878699917 35.4811728503114,129.424878736496 35.4811743840675,129.424881929132 35.4811818138357)))
5th rowMULTIPOLYGON (((129.424878699917 35.4811728503114,129.424889717304 35.4811726745176,129.424889502382 35.4811636628972,129.424878484996 35.481163838691,129.424878699917 35.4811728503114)))
ValueCountFrequency (%)
multipolygon 316
 
14.2%
35.4812159746225,129.425001180772 1
 
< 0.1%
35.4810989993689,129.424987369251 1
 
< 0.1%
35.4810988235654,129.424987154319 1
 
< 0.1%
35.4811078351853,129.424998171695 1
 
< 0.1%
35.4811080109888,129.424998386628 1
 
< 0.1%
129.424987369251 1
 
< 0.1%
35.4811170226087 1
 
< 0.1%
35.4811080109888,129.424987584184 1
 
< 0.1%
35.4811078351853,129.424987369251 1
 
< 0.1%
Other values (1904) 1904
85.4%
2024-01-14T15:58:22.003586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 6925
11.6%
1 6861
11.5%
2 6671
11.2%
9 5251
8.8%
5 4705
7.9%
3 4580
7.7%
8 4452
 
7.5%
. 3194
 
5.4%
0 2910
 
4.9%
7 2705
 
4.5%
Other values (15) 11384
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47562
79.8%
Other Punctuation 4475
 
7.5%
Uppercase Letter 3792
 
6.4%
Space Separator 1913
 
3.2%
Close Punctuation 948
 
1.6%
Open Punctuation 948
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 6925
14.6%
1 6861
14.4%
2 6671
14.0%
9 5251
11.0%
5 4705
9.9%
3 4580
9.6%
8 4452
9.4%
0 2910
6.1%
7 2705
 
5.7%
6 2502
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
O 632
16.7%
L 632
16.7%
U 316
8.3%
N 316
8.3%
G 316
8.3%
Y 316
8.3%
P 316
8.3%
I 316
8.3%
T 316
8.3%
M 316
8.3%
Other Punctuation
ValueCountFrequency (%)
. 3194
71.4%
, 1281
28.6%
Space Separator
ValueCountFrequency (%)
1913
100.0%
Close Punctuation
ValueCountFrequency (%)
) 948
100.0%
Open Punctuation
ValueCountFrequency (%)
( 948
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 55846
93.6%
Latin 3792
 
6.4%

Most frequent character per script

Common
ValueCountFrequency (%)
4 6925
12.4%
1 6861
12.3%
2 6671
11.9%
9 5251
9.4%
5 4705
8.4%
3 4580
8.2%
8 4452
8.0%
. 3194
5.7%
0 2910
 
5.2%
7 2705
 
4.8%
Other values (5) 7592
13.6%
Latin
ValueCountFrequency (%)
O 632
16.7%
L 632
16.7%
U 316
8.3%
N 316
8.3%
G 316
8.3%
Y 316
8.3%
P 316
8.3%
I 316
8.3%
T 316
8.3%
M 316
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59638
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 6925
11.6%
1 6861
11.5%
2 6671
11.2%
9 5251
8.8%
5 4705
7.9%
3 4580
7.7%
8 4452
 
7.5%
. 3194
 
5.4%
0 2910
 
4.9%
7 2705
 
4.5%
Other values (15) 11384
19.1%

GAP_XTN
Real number (ℝ)

HIGH CORRELATION 

Distinct257
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33507278
Minimum0
Maximum1
Zeros3
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-01-14T15:58:22.213056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0195
Q10.12425
median0.284
Q30.5015
95-th percentile0.84875
Maximum1
Range1
Interquartile range (IQR)0.37725

Descriptive statistics

Standard deviation0.25454186
Coefficient of variation (CV)0.75966141
Kurtosis-0.1876764
Mean0.33507278
Median Absolute Deviation (MAD)0.1785
Skewness0.75902012
Sum105.883
Variance0.06479156
MonotonicityNot monotonic
2024-01-14T15:58:22.395033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.029 4
 
1.3%
0.16 3
 
0.9%
0.044 3
 
0.9%
0.0 3
 
0.9%
0.542 3
 
0.9%
0.327 3
 
0.9%
0.717 3
 
0.9%
0.264 3
 
0.9%
0.089 3
 
0.9%
0.116 2
 
0.6%
Other values (247) 286
90.5%
ValueCountFrequency (%)
0.0 3
0.9%
0.003 2
0.6%
0.004 2
0.6%
0.005 1
 
0.3%
0.007 2
0.6%
0.008 1
 
0.3%
0.01 1
 
0.3%
0.011 1
 
0.3%
0.013 1
 
0.3%
0.014 1
 
0.3%
ValueCountFrequency (%)
1.0 2
0.6%
0.998 1
0.3%
0.997 1
0.3%
0.984 1
0.3%
0.981 1
0.3%
0.962 2
0.6%
0.935 1
0.3%
0.928 1
0.3%
0.913 1
0.3%
0.908 1
0.3%

GAP_RISK_GRD_CD
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
C
98 
E
79 
D
75 
B
61 
A
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowC
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
C 98
31.0%
E 79
25.0%
D 75
23.7%
B 61
19.3%
A 3
 
0.9%

Length

2024-01-14T15:58:22.571679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:58:22.709485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 98
31.0%
e 79
25.0%
d 75
23.7%
b 61
19.3%
a 3
 
0.9%

GAP_RISK_CN
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
주의
98 
매우위험
79 
위험
75 
보통
61 
안전
 
3

Length

Max length4
Median length2
Mean length2.5
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row매우위험
2nd row매우위험
3rd row주의
4th row위험
5th row위험

Common Values

ValueCountFrequency (%)
주의 98
31.0%
매우위험 79
25.0%
위험 75
23.7%
보통 61
19.3%
안전 3
 
0.9%

Length

2024-01-14T15:58:22.848635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:58:22.990068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주의 98
31.0%
매우위험 79
25.0%
위험 75
23.7%
보통 61
19.3%
안전 3
 
0.9%

Interactions

2024-01-14T15:58:21.237089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T15:58:23.082012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GAP_XTNGAP_RISK_GRD_CDGAP_RISK_CN
GAP_XTN1.0000.9900.990
GAP_RISK_GRD_CD0.9901.0001.000
GAP_RISK_CN0.9901.0001.000
2024-01-14T15:58:23.169921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GAP_RISK_GRD_CDGAP_RISK_CN
GAP_RISK_GRD_CD1.0001.000
GAP_RISK_CN1.0001.000
2024-01-14T15:58:23.250288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GAP_XTNGAP_RISK_GRD_CDGAP_RISK_CN
GAP_XTN1.0000.8510.851
GAP_RISK_GRD_CD0.8511.0001.000
GAP_RISK_CN0.8511.0001.000

Missing values

2024-01-14T15:58:21.342550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T15:58:21.419322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

GEOMGAP_XTNGAP_RISK_GRD_CDGAP_RISK_CN
0MULTIPOLYGON (((129.424874234203 35.4811639065166,129.424878484996 35.481163838691,129.424878270076 35.4811548270706,129.424870386739 35.4811549528568,129.424874234203 35.4811639065166)))0.532E매우위험
1MULTIPOLYGON (((129.424869335713 35.4811525069518,129.424870386739 35.4811549528568,129.424878270076 35.4811548270706,129.424878055156 35.4811458154502,129.424874723312 35.481145868613,129.424869335713 35.4811525069518)))0.981E매우위험
2MULTIPOLYGON (((129.424885776598 35.481190767495,129.424890147148 35.4811906977582,129.424889932226 35.4811816861379,129.424881929132 35.4811818138357,129.424885776598 35.481190767495)))0.21C주의
3MULTIPOLYGON (((129.424881929132 35.4811818138357,129.424889932226 35.4811816861379,129.424889717304 35.4811726745176,129.424878699917 35.4811728503114,129.424878736496 35.4811743840675,129.424881929132 35.4811818138357)))0.369D위험
4MULTIPOLYGON (((129.424878699917 35.4811728503114,129.424889717304 35.4811726745176,129.424889502382 35.4811636628972,129.424878484996 35.481163838691,129.424878699917 35.4811728503114)))0.367D위험
5MULTIPOLYGON (((129.424878484996 35.481163838691,129.424889502382 35.4811636628972,129.42488928746 35.4811546512769,129.424878270076 35.4811548270706,129.424878484996 35.481163838691)))0.312D위험
6MULTIPOLYGON (((129.424878270076 35.4811548270706,129.42488928746 35.4811546512769,129.424889072539 35.4811456396565,129.424878055156 35.4811458154502,129.424878270076 35.4811548270706)))0.148C주의
7MULTIPOLYGON (((129.424877961273 35.4811418789541,129.424878055156 35.4811458154502,129.424889072539 35.4811456396565,129.424888857617 35.4811366280361,129.424882135811 35.4811367352896,129.424877961273 35.4811418789541)))0.103C주의
8MULTIPOLYGON (((129.424897319002 35.481217628472,129.424901809307 35.4812175568239,129.424901594383 35.4812085452037,129.424893471533 35.4812086748132,129.424897319002 35.481217628472)))0.059B보통
9MULTIPOLYGON (((129.424893471533 35.4812086748132,129.424901594383 35.4812085452037,129.42490137946 35.4811995335835,129.42489036207 35.4811997093785,129.424890405734 35.4812015402209,129.424893471533 35.4812086748132)))0.201C주의
GEOMGAP_XTNGAP_RISK_GRD_CDGAP_RISK_CN
306MULTIPOLYGON (((129.425031868634 35.4811253310081,129.425042886013 35.4811251552005,129.425042671074 35.4811161435809,129.425031653696 35.4811163193885,129.425031868634 35.4811253310081)))0.307D위험
307MULTIPOLYGON (((129.425031653696 35.4811163193885,129.425042671074 35.4811161435809,129.425042456136 35.4811071319613,129.425031438759 35.4811073077688,129.425031653696 35.4811163193885)))0.219C주의
308MULTIPOLYGON (((129.425031438759 35.4811073077688,129.425042456136 35.4811071319613,129.425042241198 35.4810981203417,129.425031223822 35.4810982961491,129.425031438759 35.4811073077688)))0.061B보통
309MULTIPOLYGON (((129.425031223822 35.4810982961491,129.425042241198 35.4810981203417,129.425042026259 35.481089108722,129.425031008885 35.4810892845294,129.425031223822 35.4810982961491)))0.377D위험
310MULTIPOLYGON (((129.425031008885 35.4810892845294,129.425042026259 35.481089108722,129.425041811321 35.4810800971024,129.425030793948 35.4810802729097,129.425031008885 35.4810892845294)))0.116C주의
311MULTIPOLYGON (((129.425030793948 35.4810802729097,129.425041811321 35.4810800971024,129.425041596383 35.4810710854827,129.425030579011 35.48107126129,129.425030793948 35.4810802729097)))0.901E매우위험
312MULTIPOLYGON (((129.425030579011 35.48107126129,129.425041596383 35.4810710854827,129.425041381445 35.481062073863,129.425030364075 35.4810622496702,129.425030579011 35.48107126129)))0.908E매우위험
313MULTIPOLYGON (((129.425046754923 35.4812873643508,129.425057772324 35.4812871885411,129.425057557383 35.4812781769218,129.425046539983 35.4812783527315,129.425046754923 35.4812873643508)))0.441D위험
314MULTIPOLYGON (((129.425046539983 35.4812783527315,129.425057557383 35.4812781769218,129.425057342441 35.4812691653025,129.425046325042 35.4812693411121,129.425046539983 35.4812783527315)))0.097B보통
315MULTIPOLYGON (((129.425046325042 35.4812693411121,129.425057342441 35.4812691653025,129.4250571275 35.4812601536832,129.425046110102 35.4812603294927,129.425046325042 35.4812693411121)))0.234C주의